'''simple docstring'''




import argparse
import math
import os
from copy import deepcopy

import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn

from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel


__UpperCAmelCase					={
    "gwf-440k": {
        "url": "https://model-server.zqevans2.workers.dev/gwf-440k.ckpt",
        "sample_rate": 4_8_0_0_0,
        "sample_size": 6_5_5_3_6,
    },
    "jmann-small-190k": {
        "url": "https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt",
        "sample_rate": 4_8_0_0_0,
        "sample_size": 6_5_5_3_6,
    },
    "jmann-large-580k": {
        "url": "https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt",
        "sample_rate": 4_8_0_0_0,
        "sample_size": 1_3_1_0_7_2,
    },
    "maestro-uncond-150k": {
        "url": "https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt",
        "sample_rate": 1_6_0_0_0,
        "sample_size": 6_5_5_3_6,
    },
    "unlocked-uncond-250k": {
        "url": "https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt",
        "sample_rate": 1_6_0_0_0,
        "sample_size": 6_5_5_3_6,
    },
    "honk-140k": {
        "url": "https://model-server.zqevans2.workers.dev/honk-140k.ckpt",
        "sample_rate": 1_6_0_0_0,
        "sample_size": 6_5_5_3_6,
    },
}






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							)      ->       List[str]:
    return torch.atana(UpperCamelCase__							,    UpperCamelCase__							) / math.pi * 2






def __lowerCAmelCase					(				UpperCamelCase__							)      ->       Tuple:
    __lowerCamelCase            =    torch.sin(t * math.pi / 2							) ** 2
    __lowerCamelCase            =    (1 - sigma**2) ** 0.5
    return alpha_sigma_to_t(UpperCamelCase__							,    UpperCamelCase__							)




class        a__ (  UpperCAmelCase__					):
     pass




class        a__ (  nn.Module					):






     def __init__(					self						:						Optional[Any]    ,							a						:						List[Any]       ):





         """simple docstring"""



         super().__init__()

         __lowerCamelCase            =    DiffusionAttnUnetaD(a    ,							n_attn_layers=4       )
         __lowerCamelCase            =    deepcopy(self.diffusion       )
         __lowerCamelCase            =    torch.quasirandom.SobolEngine(1    ,							scramble=a       )






def __lowerCAmelCase					(				UpperCamelCase__							)      ->       str:
    __lowerCamelCase            =    MODELS_MAP[model_name]['''url''']
    os.system(f"""wget {url} ./"""							)

    return f"""./{model_name}.ckpt"""


__UpperCAmelCase					={
    "1": "resnets.0",
    "2": "attentions.0",
    "3": "resnets.1",
    "4": "attentions.1",
    "5": "resnets.2",
    "6": "attentions.2",
}
__UpperCAmelCase					={
    "8": "resnets.0",
    "9": "attentions.0",
    "10": "resnets.1",
    "11": "attentions.1",
    "12": "resnets.2",
    "13": "attentions.2",
}
__UpperCAmelCase					={
    "1": "resnets.0",
    "2": "attentions.0",
    "3": "resnets.1",
    "4": "attentions.1",
    "5": "resnets.2",
    "6": "attentions.2",
    "8": "resnets.3",
    "9": "attentions.3",
    "10": "resnets.4",
    "11": "attentions.4",
    "12": "resnets.5",
    "13": "attentions.5",
}
__UpperCAmelCase					={
    "0": "resnets.0",
    "1": "resnets.1",
    "2": "resnets.2",
    "4": "resnets.0",
    "5": "resnets.1",
    "6": "resnets.2",
}

__UpperCAmelCase					={
    "skip": "conv_skip",
    "main.0": "conv_1",
    "main.1": "group_norm_1",
    "main.3": "conv_2",
    "main.4": "group_norm_2",
}

__UpperCAmelCase					={
    "norm": "group_norm",
    "qkv_proj": ["query", "key", "value"],
    "out_proj": ["proj_attn"],
}






def __lowerCAmelCase					(				UpperCamelCase__							)      ->       Optional[Any]:
    if name.startswith('''skip'''							):
        return name.replace('''skip'''							,    RES_CONV_MAP['''skip''']							)

    # name has to be of format main.{digit}
    if not name.startswith('''main.'''							):
        raise ValueError(f"""ResConvBlock error with {name}"""							)

    return name.replace(name[:6]							,    RES_CONV_MAP[name[:6]]							)






def __lowerCAmelCase					(				UpperCamelCase__							)      ->       Tuple:
    for key, value in ATTN_MAP.items():
        if name.startswith(UpperCamelCase__							) and not isinstance(UpperCamelCase__							,    UpperCamelCase__							):
            return name.replace(UpperCamelCase__							,    UpperCamelCase__							)
        elif name.startswith(UpperCamelCase__							):
            return [name.replace(UpperCamelCase__							,    UpperCamelCase__							) for v in value]
    raise ValueError(f"""Attn error with {name}"""							)






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__=13							)      ->       List[Any]:
    __lowerCamelCase            =    input_string

    if string.split('''.'''							)[0] == "timestep_embed":
        return string.replace('''timestep_embed'''							,    '''time_proj'''							)

    __lowerCamelCase            =    0
    if string.startswith('''net.3.'''							):
        depth += 1
        __lowerCamelCase            =    string[6:]
    elif string.startswith('''net.'''							):
        __lowerCamelCase            =    string[4:]

    while string.startswith('''main.7.'''							):
        depth += 1
        __lowerCamelCase            =    string[7:]

    if string.startswith('''main.'''							):
        __lowerCamelCase            =    string[5:]

    # mid block
    if string[:2].isdigit():
        __lowerCamelCase            =    string[:2]
        __lowerCamelCase            =    string[2:]
    else:
        __lowerCamelCase            =    string[0]
        __lowerCamelCase            =    string[1:]

    if depth == max_depth:
        __lowerCamelCase            =    MID_NUM_TO_LAYER[layer_num]
        __lowerCamelCase            =    '''mid_block'''
    elif depth > 0 and int(UpperCamelCase__							) < 7:
        __lowerCamelCase            =    DOWN_NUM_TO_LAYER[layer_num]
        __lowerCamelCase            =    f"""down_blocks.{depth}"""
    elif depth > 0 and int(UpperCamelCase__							) > 7:
        __lowerCamelCase            =    UP_NUM_TO_LAYER[layer_num]
        __lowerCamelCase            =    f"""up_blocks.{max_depth - depth - 1}"""
    elif depth == 0:
        __lowerCamelCase            =    DEPTH_0_TO_LAYER[layer_num]
        __lowerCamelCase            =    f"""up_blocks.{max_depth - 1}""" if int(UpperCamelCase__							) > 3 else '''down_blocks.0'''

    if not string_left.startswith('''.'''							):
        raise ValueError(f"""Naming error with {input_string} and string_left: {string_left}."""							)

    __lowerCamelCase            =    string_left[1:]

    if "resnets" in new_layer:
        __lowerCamelCase            =    convert_resconv_naming(UpperCamelCase__							)
    elif "attentions" in new_layer:
        __lowerCamelCase            =    convert_attn_naming(UpperCamelCase__							)
        __lowerCamelCase            =    new_string_left

    if not isinstance(UpperCamelCase__							,    UpperCamelCase__							):
        __lowerCamelCase            =    prefix + '''.''' + new_layer + '''.''' + string_left
    else:
        __lowerCamelCase            =    [prefix + '''.''' + new_layer + '''.''' + s for s in string_left]
    return new_string






def __lowerCAmelCase					(				UpperCamelCase__							)      ->       List[str]:
    __lowerCamelCase            =    {}
    for k, v in state_dict.items():
        if k.endswith('''kernel'''							):
            # up- and downsample layers, don't have trainable weights
            continue

        __lowerCamelCase            =    rename(UpperCamelCase__							)

        # check if we need to transform from Conv => Linear for attention
        if isinstance(UpperCamelCase__							,    UpperCamelCase__							):
            __lowerCamelCase            =    transform_conv_attns(UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)
        else:
            __lowerCamelCase            =    v

    return new_state_dict






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)      ->       str:
    if len(UpperCamelCase__							) == 1:
        if len(v.shape							) == 3:
            # weight
            __lowerCamelCase            =    v[:, :, 0]
        else:
            # bias
            __lowerCamelCase            =    v
    else:
        # qkv matrices
        __lowerCamelCase            =    v.shape[0]
        __lowerCamelCase            =    trippled_shape // 3
        for i in range(3							):
            if len(v.shape							) == 3:
                __lowerCamelCase            =    v[i * single_shape : (i + 1) * single_shape, :, 0]
            else:
                __lowerCamelCase            =    v[i * single_shape : (i + 1) * single_shape]
    return new_state_dict






def __lowerCAmelCase					(				UpperCamelCase__							)      ->       int:
    __lowerCamelCase            =    torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''							)

    __lowerCamelCase            =    args.model_path.split('''/'''							)[-1].split('''.'''							)[0]
    if not os.path.isfile(args.model_path							):
        assert (
            model_name == args.model_path
        ), f"""Make sure to provide one of the official model names {MODELS_MAP.keys()}"""
        __lowerCamelCase            =    download(UpperCamelCase__							)

    __lowerCamelCase            =    MODELS_MAP[model_name]['''sample_rate''']
    __lowerCamelCase            =    MODELS_MAP[model_name]['''sample_size''']

    __lowerCamelCase            =    Object()
    __lowerCamelCase            =    sample_size
    __lowerCamelCase            =    sample_rate
    __lowerCamelCase            =    0

    __lowerCamelCase            =    UNetaDModel(sample_size=UpperCamelCase__							,    sample_rate=UpperCamelCase__							)
    __lowerCamelCase            =    diffusers_model.state_dict()

    __lowerCamelCase            =    DiffusionUncond(UpperCamelCase__							)
    orig_model.load_state_dict(torch.load(args.model_path							,    map_location=UpperCamelCase__							)['''state_dict''']							)
    __lowerCamelCase            =    orig_model.diffusion_ema.eval()
    __lowerCamelCase            =    orig_model.state_dict()
    __lowerCamelCase            =    rename_orig_weights(UpperCamelCase__							)

    __lowerCamelCase            =    set(renamed_state_dict.keys()							) - set(diffusers_state_dict.keys()							)
    __lowerCamelCase            =    set(diffusers_state_dict.keys()							) - set(renamed_state_dict.keys()							)

    assert len(UpperCamelCase__							) == 0, f"""Problem with {renamed_minus_diffusers}"""
    assert all(k.endswith('''kernel'''							) for k in list(UpperCamelCase__							)							), f"""Problem with {diffusers_minus_renamed}"""

    for key, value in renamed_state_dict.items():
        assert (
            diffusers_state_dict[key].squeeze().shape == value.squeeze().shape
        ), f"""Shape for {key} doesn't match. Diffusers: {diffusers_state_dict[key].shape} vs. {value.shape}"""
        if key == "time_proj.weight":
            __lowerCamelCase            =    value.squeeze()

        __lowerCamelCase            =    value

    diffusers_model.load_state_dict(UpperCamelCase__							)

    __lowerCamelCase            =    1_00
    __lowerCamelCase            =    33

    __lowerCamelCase            =    IPNDMScheduler(num_train_timesteps=UpperCamelCase__							)

    __lowerCamelCase            =    torch.manual_seed(UpperCamelCase__							)
    __lowerCamelCase            =    torch.randn([1, 2, config.sample_size]							,    generator=UpperCamelCase__							).to(UpperCamelCase__							)

    __lowerCamelCase            =    torch.linspace(1							,    0							,    steps + 1							,    device=UpperCamelCase__							)[:-1]
    __lowerCamelCase            =    get_crash_schedule(UpperCamelCase__							)

    __lowerCamelCase            =    DanceDiffusionPipeline(unet=UpperCamelCase__							,    scheduler=UpperCamelCase__							)

    __lowerCamelCase            =    torch.manual_seed(33							)
    __lowerCamelCase            =    pipe(num_inference_steps=UpperCamelCase__							,    generator=UpperCamelCase__							).audios

    __lowerCamelCase            =    sampling.iplms_sample(UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							,    {}							)
    __lowerCamelCase            =    generated.clamp(-1							,    1							)

    __lowerCamelCase            =    (generated - audio).abs().sum()
    __lowerCamelCase            =    (generated - audio).abs().max()

    if args.save:
        pipe.save_pretrained(args.checkpoint_path							)

    print('''Diff sum'''							,    UpperCamelCase__							)
    print('''Diff max'''							,    UpperCamelCase__							)

    assert diff_max < 1E-3, f"""Diff max: {diff_max} is too much :-/"""

    print(f"""Conversion for {model_name} successful!"""							)


if __name__ == "__main__":
  __UpperCAmelCase					=argparse.ArgumentParser()

  parser.add_argument("--model_path", default=None, type=str, required=True, help="Path to the model to convert.")
  parser.add_argument(
      "--save", default=True, type=bool, required=False, help="Whether to save the converted model or not."
  )
  parser.add_argument("--checkpoint_path", default=None, type=str, required=True, help="Path to the output model.")
  __UpperCAmelCase					=parser.parse_args()

  main(args)

'''simple docstring'''




def __lowerCAmelCase					(				UpperCamelCase__							)      ->       bool:
    if not isinstance(UpperCamelCase__							,    UpperCamelCase__							):
        raise ValueError('''Input series is not valid, valid series - [2, 4, 6]'''							)
    if len(UpperCamelCase__							) == 0:
        raise ValueError('''Input list must be a non empty list'''							)
    if len(UpperCamelCase__							) == 1:
        return True
    __lowerCamelCase            =    series[1] - series[0]
    for index in range(len(UpperCamelCase__							) - 1							):
        if series[index + 1] - series[index] != common_diff:
            return False
    return True






def __lowerCAmelCase					(				UpperCamelCase__							)      ->       float:
    if not isinstance(UpperCamelCase__							,    UpperCamelCase__							):
        raise ValueError('''Input series is not valid, valid series - [2, 4, 6]'''							)
    if len(UpperCamelCase__							) == 0:
        raise ValueError('''Input list must be a non empty list'''							)
    __lowerCamelCase            =    0
    for val in series:
        answer += val
    return answer / len(UpperCamelCase__							)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
